In this particular example, because we have not specified a "path" constructor option, the JSON data set will attempt to flatten the top-level object. Since we want to also include the data from the "image" nested structure, we specify the path to the data which is simply "image". The properties within the nested "image" structure are now accessible from within the data set.
Notice that the names of the columns are all prefixed by "image. You can specify multiple paths in the "subPaths" constructor option. So if you wanted to include both "image" and "thumbnail" in the flattening process, you simply pass an array of strings:.
This example shows the use of the "path" constructor option to extract out the data items. This is nothing different from some of the previous examples, but we will build on this in the next example. An abbreviated version of the JSON data is included here for reference. You can see the full JSON data used by this example here. In this example, we are simply going to list the types of items in our JSON object.
Must Learn Expand child menu Expand. Big Data Expand child menu Expand. Live Project Expand child menu Expand. AI Expand child menu Expand. Toggle Menu Close. Search for: Search. It is a collection of key-value pairs and always separated by a comma and enclosed in curly brackets. Index can be used to both filter and sort docs if includes equality conditions on all prefix keys that precede sort keys. Example below - no longer able to use index for sorting, although it can for fitering:.
For each entry in array, server will create separate index key. From eg above, would have 3 index entries all pointing to the same doc: T-Shirts, Clothing,and Apparel. In addition to indexing on scalar values such as strings, can also index on nested docs, eg:. With sample doc above, could have index on productName and stock. Take care when creating multikey indexes, ensure arrays don't grow too large, this causes index to get overly large, which then may not be able to load entirely in memory, forcing query to go to disk.
Now insert a document where stock field is an array instead of embedded doc, then run query again:. Still doing index scan,but this time, multikey is true, because stock is an array field in one of the documents. May want to index only a portion of documents. Can reduce performance costs of creating and maintaining indexes.
Create partial index - only index on city and cuisine if restarant has 3. This reduces number of index keys mongo needs to store - reduces memory requirement. Useful when index has grown too large to fit into memory. Sparse indexes are special case of partial indexes. Sparse index only indexes doc where a index field exists in doc, eg:. Partial indexes are more expressive than sparse indexes - can define filter expression that checksk for existence of fields that are not index keys, eg:.
To use partial index, query must be guaranteed to match subset of docs specified by filter expression. Otherwise server might miss results where matching docs not indexed. To make index used, need to include predicate that matches partial filter expression, stars in our example:.
This query would find the matching doc if you knew exactly what string to look for:. But users unlikely to know exact string to search for, could use regex - works but bad for performance even with index:.
Text indexing similar to multikey index. Strategy to minimize text index size is to use compound index, limits number of text keys that need to be examined by limiting on category when searching, eg:. Text queries logically or each delimited word. Project textScore to return results. Specify language specific rules for string comparison, such as letter case and accents. Defined with options:. Can specify a different collation on a given request or index creation.
For index, will override default and collection level collations. Set strength to 1 for primary level of comparison i. Very fast but blocks all incoming operations to database containing collection on which index is being built. Don't block operations but slower to build index. Given 1M docs in collection, will take considerable amount of time to build index. Exactly how long depends on cardinality of fields and other operations going on at the same time.
Create index in background, by default background is set to false so must set it explicitly otherwise:. Background option can be used on standalone mongod, or primary or secondaries in replica set. Note even though index is being created in background, shell will block until command returns. To see status, open another shell and check for current operations, passing in a filter to limit results.
This looks for commands that are creating indexes or inserting documents into an index:. Notice each operation has an opid , will need this if want to kill the operation before it completes, eg:. For any given query, could have many different query plans based on available indexes. Then determines of these, which are viable to satisfy query - i. MongoDB has empirical query planner - trial period where each candidate plan is executed over short period of time.
Planner evaluates which performs the best:. When run explain , winning plan will show the best plan that was evaluated. Other plans will show up under rejected section. Not efficient to run trial plans for every incoming query since lots of them have the same "shape".
Over time, collection and indexes may change, therefore plan cache will evict the cache occasionally. Plans can be evicted when:. Then use explainable object to run query - more convienient can run multiple queries from same exp object:. Most verbose - use when want to look at alternate plans that were considered by planner but rejected - WILL execute query:.
Now will also see rejectedPlans in explain output - showing other plans that were considered. This shows up because we now have multiple indexes for planner to evaluate. Above query returned 7 docs, determine avg size of doc, multiply. More complex example, running explain on sharded cluster, using mlaunch from mtools to setup sharded cluster:. Now when query run on mongos, mongos itself doesn't do the work, it sends the query to each shard.
Each shard evaluates query, selects plan, then results aggregated on mongos. Would expect same plan chosen on each shard. But each shard may choose a different plan, for eg if it has more or less data to process. Can force mongo to use a particular index by overriding mongo's default index selection with hint. To force it to use index you want, append hint method to query, passing in "shape" of desired index:. Mongo's query optimizer generally picks the correct index.
If it does pick not the best one, probably because there are too many different indexes on collection - better to review why there are so many indexes and consider if some are superfluous and could be removed.
Compass will display index size for each collection in a database. Selecting a particular collection will break down index size for each index on that collection. Disk to store index information. Not generally an issue because if not enough space on disk for index, wouldn't get created.
After index created, disk space requirement is a function of dadta. Therefore you would run out of disk space for collections before encoutering issues with indexes. However, if operating with separate disks for indexes vs collection, do need to ensure disk allocated for indexes is large enough.
Memory to operate with those data structures. This is most intensive resource utilization for index usage. Deployments should be sized to accomodate all indexes in RAM. If not enough space in RAM for index, lots of disk access will be required to traverse index. As you traverse that space, will slide pages into positions that are no longer in memory, therefore need to allocate those to memory and flush out info to disk:.
From mongo shell, get collection stats, passing in flag to also include index details:. General rule is to always have enough memory to allocate indexes. Whitespace can be inserted between any pair of tokens.
Excepting a few encoding details, that completely describes the language. Introducing JSON. In various languages, this is realized as an object , record, struct, dictionary, hash table, keyed list, or associative array. An ordered list of values. In most languages, this is realized as an array , vector, list, or sequence.
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